UV dosimetry system for safe UV exposure

ABSTRACT

A UV exposure dosimetry system includes at least one UV sensor that accurately measures the UV irradiance intensity. The UV dosimetry system integrates the measured UV irradiance intensity over time to calculate the real-time UV dosage and the vitamin D production by taking into account factors comprising UV sensor location, body surface area, clothing coverage, and sunscreen usage. Based on the measurement, the system can predict the time remaining to skin burn and the time remaining to reach daily goal of vitamin D production. The system also calculates the UV index in real-time, and can crowd source the measured data in a network. The UV dosimetry system supports multi-user control through an advanced and user friendly input and output interface.

CLAIM OF PRIORITY

This application claims the benefit of priority under 35 U.S.C. §119(e)of U.S. Provisional Application No. 61/918,153, entitled “UV DOSIMETRYSYSTEM”, filed on Dec. 19, 2013, which is hereby incorporated byreference.

1. FIELD OF THE INVENTION

The present invention relates generally to a system and methods formeasuring the ultraviolet (UV) irradiation dosage and usage of suchinformation for calculating the vitamin D production, for analyzing anddisplaying related UV information, and for guiding users to achieveoptimal UV exposure.

2. BACKGROUND

Optimal Balance of Sun Exposure

UV exposure is known to have both detrimental and beneficial effect.Over exposure to sunlight can cause sunburn, skin aging, and skincancer, whereas insufficient sunlight exposure can lead to vitamin Ddeficiency, which is associated with many health maladies. Therefore,there has been a growing consensus among many public healthorganizations that there needs to be a balance between the risks ofhaving too much and the risks of having too little sunlight. However,the goal of achieving optimal balance of UV exposure remains elusive,since there is no definition of what is considered to be the optimal UVdosage, and consequently there is no quantitative means to assesswhether a person's UV exposure is optimal or suboptimal. Therefore,there is a need to provide a system and method for quantitativeevaluation the optimal balance of UV exposure, by taking into accountboth skin damage and vitamin D production effects of UV light. However,to develop a solution for this problem, there are several additionalchallenges that need to be overcome, as described below.

Safe Sun Exposure Time

One of the challenges is to quantify the damaging effect of UV exposure,which can be immediate and long-term. Sunburn is noticeable 3-4 hoursafter over exposure of UV light, peaking at around 24 hours. Thelong-term effects of excessive UV exposure include photo-aging,immunosuppression, and carcinogenicity. Since sunburn is a frequentdetrimental effect on human skin, the CIE erythemal action spectrum isoften recommended for use in assessing the skin damaging effect of UVradiation. The minimal erythemal dose (MED) is used to describe theerythemal potential of UV radiation, and 1 MED is defined as theeffective UV dose that causes a perceptible reddening of previouslyunexposed human skin. The MED is known to vary between individuals, andis affected by many factors such as the skin type. Therefore,personalized UV irradiance dose monitoring is important. For a UVmonitoring system, it is important to predict how long a person cansafely stay exposed to sunlight before getting sunburn. The time to skinburn is a dynamic parameter. It is affected by the time varyingintensity of UV irradiance. It also depends on the accumulated UV dosealready received by the person. Moreover, it can be modified by theperson's behaviors, such as seeking the shade, changing clothingcoverage, applying the sunscreen, etc. To the best knowledge of theinventors, there is no existing solution for dynamic estimation of aperson's safe sun exposure time.

Cutaneous Vitamin D Synthesis

Another challenge is to quantify the UV induced cutaneous vitamin Dsynthesis. Vitamin D, commonly known as the sunshine vitamin, actuallyfunctions as a hormone. Its main biologic function in people is tomaintain serum calcium and phosphorus concentration within the normalrange. Sufficient vitamin D is not only important for bone health, butalso may decrease the incidence of diabetes, inhibit some autoimmunediseases such as multiple sclerosis, reduce the mortality from commoncancers and cardiovascular diseases, among many other health benefits.However, studies have shown that many people do not go outside enough tomeet their minimum vitamin D needs. In fact, vitamin D deficiency is nowrecognized as a pandemic. Therefore, there is a need for a solution tocalculate daily vitamin D production resulting from UV exposure, and toprovide user useful information to determine if vitamin D supplement isnecessary, and if so, how much is necessary.

Effective Sun Protection Factor

A person's UV exposure is affected by the usage of sunscreen. The sunprotection factor (SPF) is widely used to measure the effectiveness of asunscreen in affording photoprotection to subjects exposed to sources ofUV radiation that may produce cutaneous erythema. According to the U.S.Food and Drug Administration and international protocols, SPF shall bedetermined by using a prescribed sunscreen application thickness of 2.0mg/cm². However, many studies have shown that users typically apply muchless sunscreen during leisure time than the suggested 2.0 mg/cm²,typically between 0.5 and 1.5 mg/cm². Moreover, after a sunscreen isapplied to the skin, it is commonly removed to a greater or lesserextend because of activities such as swimming, toweling, or excessivesweating and rubbing. Thus, frequent and regular reapplication ofsunscreen is often recommended for users who experience extended periodof sun exposure, but the optimal frequency and amount of sunscreenreapplication remain inconclusive. For these reasons, the effective SPFof a sunscreen product often actually deviates from the labeled SPF.Therefore, a solution is needed for dynamic calculation the effectiveSPF of the applied sunscreen. This is important for at least tworeasons. First, it allows more accurate estimation of the accumulated UVdose received by the subject, thus can help user to better protectagainst skin damage caused by over exposure of UV radiance. Second,since a high SPF sunscreen dramatically reduces the skin's capability tosynthesize vitamin D, knowing the effective SPF allows more accurateestimation of the vitamin D production.

Exposed Body Surface Area

Because only skin surface exposed to sunlight can synthesize vitamin D,different clothing choices can significantly affect the amount ofvitamin D production in a person. Therefore, to estimate the amount ofcutaneous vitamin D synthesis based on the UV dosage, it is important toknow the percentage of exposed body surface area, taking into account ofvarious options of clothing coverage. Consequently, there is a need fora solution to efficiently calculate the exposed body surface areacorresponding to common clothing choices in order to accurately measurea user's vitamin D production.

Reliable UV Sensor Measurement

Yet another challenge is to reject or correct unreliable UV sensormeasurement. If the UV sensor is not aligned with the direct solarirradiance, the sensor measurement may underestimate the UV intensity atsurfaces that are normal to the sunlight. In particular, the anglesensitivity of UV sensor with respect to sunlight poses a practicalchallenge for a wearable UV dosimeter, since its orientation can changeanytime as the user varies position and/or posture while the sunirradiance angle also varies constantly. Even subtle change of sensororientation and/or the shade coverage caused by user movement candramatically change the sensor measurement. Thus the direct measurementfrom the wearable UV dosimeter may be less than the actual UV intensityon the user's body surface that is normal to the sun light. Therefore,in order to more accurately estimate the UV dosage received by the user,there is a need for a solution to filter out the unreliable UV sensormeasurement.

Sun Exposure Time

Solar radiation has been used since ancient times to treat variousdiseases. Although the beneficial effects of solar radiation are mainlymediated via UV induced vitamin D production in skin, several otherpathways may exist for the action of UV radiation on humans. Inparticular, exposure to sunlight has mood enhancing effect, which isbelieved to be mediated through the release of serotonin. Research hasshown that the rate of production of serotonin by the brain was directlyrelated to the prevailing duration of bright sunlight, and rose rapidlywith increased luminosity. The concept—that sunlight has beneficialeffects on the serotonin system and the mood and stability of humans—isconsistent with the idea that serotonin is involved in homeostasis inhumans and contributes to the emergence of mind. Therefore, there is aneed for a solution to calculate the daily sun exposure time (SET), thusallowing user to potentially correlate SET with her/his mood of the day.Such a feature may not only reveal the likely correlation between moodand SET, but also provides the user guidance to enhance mood byconsciously adjusting SET.

Prediction of Optimal Sun Exposure Time

While it is helpful for a person to obtain instant feedback on receivedUV dose and cutaneous vitamin D production and use such information as aguidance to optimize sun exposure, more benefits can be gained if theperson knows in advance how to spend time outdoors in the near futurefor optimal sun experience. For example, it would be very helpful if aperson knows what time and how long to stay outside in the next few daysto avoid sun damage and to generate sufficient vitamin D. Therefore,there is a need for a solution to provide the user predictiveinformation on optimal sun exposure in the near future, so that the usercan plan ahead based on such information.

3. SUMMARY OF THE INVENTION

The challenges described above are solved by the present invention asdescribed below.

It is an objective of this invention to provide a system and method formonitoring a person's UV exposure, and using such information to providefeedback and guidance to the user for achieving optimal balance betweenthe risk and benefits of UV exposure.

It is another objective of this invention to provide a system and methodfor dynamic estimation of a person's safe sun exposure time.

It is a further objective of this invention to provide a system andmethod for calculating a person's vitamin D production resulting from UVexposure.

It is yet a further objective of this invention to provide a system andmethod for dynamic calculating the effective SPF of the appliedsunscreen.

Another objective of this invention is to provide a system and methodfor calculating a person's exposed body surface area corresponding tocommon clothing choices.

Yet a further objective of this invention is to provide a system andmethod for calculating a person's the daily sun exposure time.

Yet it is also an objective of this invention to provide a system andmethod for providing predictive information to the user to guide optimalsun exposure in the near future.

According to this invention, a personal UV dosimetry system comprises awearable unit and a mobile computing device. The wearable unit comprisesa circuitry for measuring the irradiating UV intensity, and the wearableunit is capable of wirelessly communicating with the mobile computingdevice. The mobile computing device has the software application thatcan display, store, edit, analyze, and provide summary report of thedata collected by the wearable unit, and may further communicate with aremote server, which can save the measured data in a secure database,and such data becomes accessible from other mobile devices or directlyfrom a webpage protected by a password.

By analyzing the measured UV intensity data, the UV dosimetry system notonly can calculate the real-time UV index (UVI), but also UV doseactually received by the subject and vitamin D production within a timeperiod, taking into account of various factors such as the skin type,the sunscreen usage, the sensor location, etc.

By analyzing data about UV dose and vitamin D production, the UVdosimetry system calculates a new metric that can quantitativelyevaluate the optimal balance of UV exposure, by taking into account ofboth skin damage and vitamin D production effects of UV light. The newmetric is preferably calculated and displayed in real-time by a wearableUV dosimeter that provides a visual feedback and guidance to the userfor achieving optimal balance between the risk and benefits of UVexposure. The UV dosimetry system has a number of unique features,including real-time estimation of the safe sun exposure time, andcalculation of cutaneous vitamin D production due to UV exposure. The UVdosimetry system implements advanced algorithms to improve the accuracyof calculation by taking into account of dynamic behavior of SPF,exposed body surface area, and signal processing of UV intensity data toreject or correct unreliable measurement. The UV dosimetry system alsocalculates the daily sun exposure time (SET), thus allowing user topotentially correlate SET with her/his mood of the day. Moreover, the UVdosimetry system can be individually customized by taking into accountvarious factors, such as the subject's skin type, age, body surfacearea, and clothing coverage, the usage of sunscreen, the location of thewearable unit, etc. In addition, the UV dosimetry system can providepredictive information based on available forecast data to advise userwhat time and how long to stay outside in the next few days to avoid sundamage and to generate sufficient vitamin D.

The unique advantages of the present invention will be appreciated bypeople of ordinary skill in the art after referring to the writtendescription of the invention in conjunction with the illustrativedrawings.

4. BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the high level block diagram of the UV dosimeter systemcomprising a wearable unit and a mobile computing device.

FIG. 2 shows the high level operation flowchart of the applicationsoftware program running on the wearable unit and the applicationsoftware program running on the mobile computing device, and thecommunication between thereof.

FIG. 3 shows the 3D view of UV exposure score (UVES) as a function ofpvd (percentage of vitamin D production toward the user-defined dailygoal) and puv (percentage of accumulated UV dose against the limit ofsafe UV dose).

FIG. 4 illustrates the concept of estimating the slope of UV doseaccumulation based on linear regression of the most recent UV doseaccumulation curve.

FIG. 5 illustrates the concept of adjusting vector length forcalculating the slope of UV dose curve that has a flat segment followedby an upward increase.

FIG. 6 illustrates that the actual UV intensity irradiated on thesubject over a short period of time can be extrapolated based on thelimited numbers of correct measurement of UV intensity obtained duringthat short time window.

FIG. 7 illustrates the concept of temporal interpolation of UVI or UVintensity at any given time of day based on the forecast UVI data.

5. DETAILED DESCRIPTION

General Description of the System Components

According to a typical embodiment of this invention, the personal UVdosimetry system comprises a UV sensing unit and a mobile computingunit. The UV sensing unit comprises a circuitry for measuring theirradiating UV intensity. The UV sensing unit is capable of wirelesslyand bi-directionally communicating with the mobile computing unit, suchas a smart phone or a tablet computer. As known in the art, the mobilecomputing unit usually has rechargeable battery, a built-in camera, alocation/navigation system such as the Global Positioning System (GPS),a user interface for receiving user input and generating various typesof output, including but are not limited to, high resolution display,user notifications (e.g. via sound, vibration, text message, etc.). Themobile computing unit runs an operating system (e.g. iOS, Android, etc.)and is capable of wireless connection to a communication network. Themobile computing unit has the software application that can display,store, edit, analyze, and provide summary report of the data collectedby the UV sensing unit. The UV measurement data collected by the UVsensing unit and analyzed by the mobile computing unit includes not onlyUVI, but also UV dose actually received by the subject and vitamin Dproduction within a time period, taking into account of various factorssuch as skin type, sunscreen usage, etc. In addition, the mobilecomputing unit can simultaneously tracks multiple users and/or multipleUV sensing units. Yet according to another embodiment of this invention,the UV dosimetry system is an integrated device that comprises both theUV sensing unit and the mobile computing unit. In other words, all thefunctions of the mobile computing unit described above are physicallyintegrated together with the UV sensing unit, thus eliminating the needof two separate units and the wireless communication between them. Forthe purpose of illustration, we describe in the following the UVdosimetry system comprising two separate units (i.e. the UV sensing unitand the mobile computing device) as an example, while it should beunderstood that the same concept also applies to the UV dosimetry systemwith integrated UV sensing unit and the mobile computing unit.

According to one embodiment of this invention, the UV sensing unit is awearable unit, which is a stand-alone, miniature device with aestheticdesign, and is preferably waterproof. The wearable unit has a pluralityof connecting mechanisms (e.g. clip, button, adhesive surface, etc.)that enable it to be easily adapted to be worn by an individual atvarious body parts. For example, the wearable unit can be worn as aclothing button, or a necklace, or wrist band, or hat accessory, etc.

According to another embodiment of this invention, the UV sensing unitis integrated with another article of manufacture. For example, the UVsensing unit can be a built-in unit of a cooler, a beach chair, anumbrella, a pair of eye classes or goggles, a headband, a roof-topweather station, etc. In another example, the UV sensing unit can bewoven into the clothing such as shirts, jackets, scarf, etc., to be partof the so-called smart clothing. By embedding the UV sensing unit intextiles, the UV dosimetry system can become truly wearable.

In the following, we describe the UV dosimetry system by referring awearable unit as an example of the UV sensing unit, although it shouldbe understood that the UV sensing unit can take different forms asdisclosed above.

FIG. 1 shows the high level block diagram of the UV dosimeter systemcomprising a wearable unit 200 and a mobile computing device 100. The UVradiation can be detected by one or more built-in UV sensors 210, whichtransform incident radiant UV signals into standard electrical signals.Semiconductor UV sensors fabricated using Silicon, Silicon-Carbide,Gallium-Nitride, Gallium-Arsenide and Germanium may be used as UVsensors due to their wide band-gap properties. The UV signalscorresponding to the UV radiation intensity may be further digitized byan analog-to-digital converter 230. Other sensors 220 such astemperature sensors and humidity sensors can be included into the unitfor enhanced system features. All detected sensing signals are processedby a microprocessor 250, and the acquired sensor data can be temporarilystored in a local memory circuit. The arranged data is then wirelesslysent to the mobile computing device for post data processing. A wirelesscommunication unit 240 employing a wireless technology standard such asBluetooth technology can be used to wirelessly transmit and receive databetween the wearable unit 200 and the mobile computing device 100.Battery cells 270 such as a battery button cell can be used to power thewearable unit 200. Energy harvesters such as thermal electric generatoror solar cell can be used as an alternative source of energy to powerthe wearable unit. The battery measurement block 260 may be included forenhanced features including monitoring the battery usage and determiningdevice false operating conditions.

General Description of the System Operation

As shown in FIG. 2, the application program 600 running on the wearableunit controls the operation of the wearable unit 200. After the wearableunit 200 is turned on, the application program 600 starts advertisingthe data 610 through the wireless communication protocol 610 to pairwith the mobile computing unit 100. The mobile application program 500running on the mobile computing device 100 then tries to discover theadvertising data pocket 510 to pair with the wearable unit 200. Once thebroadcasting data is detected, an authentication process begins whereboth wearable unit 200 and mobile computing device 100 exchange the datato establish a connection. Several connection processes may be attempteduntil the connection is established, and the pair of wearable unit 200and the mobile computing unit 100 identify and register with each other(620, 520) by respective application programs. The mobile applicationprogram 500 can notify users for a status of this pairing process. Themobile application program 500 can keep a record of the previousregistered wearable units 200, thus the pairing process can be expeditedin the future. Through the mobile application program 500, the user canedit modes of operation as well as personal information 525 such as theskin type, age, wearable position, and sun protection factor (SPF), etc.Some user information can be detected by employing a mobile computingperipheral unit. For example, the skin type may be detected by analyzingan image captured by a mobile computing camera. SPF lotion data can bedetected by scanning the barcode on a lotion bottle. Based on receivedUV data 535 measured and sent from the wearable unit 200 and the entereduser information 525, the mobile application program 500 can calculatethe UVI, UV dose, and the amount of vitamin D production 540. Users canchange the personal information 555 such as sunscreen usage and clothingcoverage at any time so that the updated user information can be usedfor calculating UV dose and vitamin D production. User can also checkthe UV dose and related information such as vitamin D production throughthe mobile application program 500 anytime. The application programfeatures will be described later in this disclosure. The wireless datacommunication between the wearable unit 200 and the mobile computingdevice 100 is automatically running in the background to make the mobileapplication program 500 user friendly and easy to use.

General Description of the Communication Protocol

Both mobile computing device 100 and wearable unit 200 are linkedtogether through a communication protocol as demonstrated in the highlevel flowchart shown in FIG. 2. After the pairing connection process isestablished, the user programmable modes of operation 525 correspondingto commands and configurations for the wearable unit 200 such as the UVsample time, number of the UV data samples and UV data resolution arewirelessly transmitted 530 to the wearable unit 200 to determine ifconfiguration update is necessary 625. Based on the received commands625, the wearable unit 200 will either keep previous configurations 630or set new configurations 635 for measuring UV data 640. Theseconfigurations determine how the UV data is measured and how often thedata is wirelessly transmitted to the mobile computing device 100. Forexample, if a user wants to know the current UVI irradiating on thewearable unit 200, the data can be immediately sampled and transmittedto the mobile computing device 100. If the mobile computing device 100is not in close proximity of the user 645, the sampled UV data can betemporarily stored in the local memory 650 of the wearable unit 200 forlater transmission whenever the mobile computing device 100 is withinthe range to establish a pairing connection. In order to save thewearable unit battery consumption, all or parts of the electroniccircuitries especially the wireless communication circuit could bepowered down into a sleep mode 665, which consumes a minimal power. Thewearable unit 200 goes into sleep mode whenever there is no active datasampling/processing or communication with the mobile computing device100, and typically wakes up automatically 675 according to configuredsampling period and communication protocol. A limited data processingand calculation on the wearable unit 200 can be implemented to reduce afirmware complexity on the microprocessor 250 and minimize the wake-uptime for power saving. Therefore, most of the post data processing suchas calculation of UV dose and vitamin D generation can be performed inthe mobile computing device 100 as illustrated in FIG. 2. The results ofthe calculation can be sent back to the wearable unit 200 throughwireless communication. Alternatively, if the mobile computing device100 is out of the communication range of the wearable unit 200, the dataprocessing and calculation could be performed by the microprocessor 250on the wearable unit 200. If the calculated data results in a necessaryuser alert 545, the alarm mechanism is activated. The alarm mechanismcould be an alarm sound, flashing screen/light or vibration generated bythe mobile computing device 100, or the wearable unit 200, or both. Forexample, the alarm can be generated by the mobile computing device 100to display related alarm information 550. Meanwhile, an alarm command660 can be sent to the wearable unit 200 which also generates a userperceptible light and/or vibration alert 670.

As shown in FIG. 2, communication between the wearable unit 200 and themobile computing device 100 is periodic for maintaining connection. Thepolling mechanism where one device broadcasts data and waits for anotherdevice acknowledges is used for notifying the connection status. If thetime between broadcasting and waiting for acknowledgment is beyond aprogrammable communication time-out, connection is lost. With severalfailing reconnection attempts, the sampled UV data is then stored in thelocal memory circuit 650. Whenever the mobile computing device 100 iswithin the range 645 to establish a successful connection, thepreviously stored data is automatically transmitted 655 to the mobilecomputing device 100. By evaluating the received signal strength sent bythe wearable unit 200, the mobile device 100 can implement theelectronic leash function to estimate the distance to the wearable unit200 and determine if it is within or outside the communication range.

Since the wearable unit 200 and the mobile computing device 100 arecoupled together via wireless communication, the periodic status updatessuch as electronic leash notification, battery indication and memoryindication can be wirelessly transmitted along with the measured UVdata. Alarm can be generated by the wearable unit 200, or the mobilecomputing device 100, or both to alert the user of abnormal systemconditions (e.g. memory full, low battery, communication lost, etc.).Therefore, it allows the UV dosimetry system operate without anyinterruptions due to electronic unleash, low battery or lack of memory.As a result, the accuracy and reliability of the system increases.

General Description of the User Interface

The wearable unit is by default in a sleep mode and the user canactivate the measurement of the UVI, UV dose, and vitamin D production.The mobile device app can start, pause, resume, stop, and reset themeasurement. The wearable unit can also start, pause, resume, and resetthe measurements with user input, such as finger taps on a touch sensingarea. For example, different numbers and/or sequences of taps can meandifferent commands.

The mobile device and the wearable unit can trigger sound, light, orvibration alarms when the accumulated UV dose is close to the harmfullevel. The wearable unit can also indicate the status or measurementresults such as UVI, for example, with a few LEDs laid out in a circleor line. The mobile device can instantly query the measurements from thewearable unit. It can also display, store, edit, analyze, and providesummary report of the data collected by the wearable unit.

The mobile application syncs with a remote server to save the sensormeasurement data in a secure database. This data becomes accessible fromother mobile devices or directly from a webpage protected by a password.Using a software application, the data can be analyzed and presented ineasy to understand graphs, tables, and summary reports. The user canshare the data through social media, email, or text message.

Annotations can be added to the data for tagging purposes. For example,through annotation, the user can then remember when the data has beentaken. The user can also annotate relevant events such as applying asunscreen lotion, changing clothes, staying indoors, taking vitamin Dsupplement, etc.

The UV dosimetry system can accurately measure the location specific UVIin real time, which is more precise than the UVI forecast predicted byweather services. The UVI measurement by a user wearing the wearableunit can be posted in crowd sourcing websites or on a dedicated serverand shared in the mobile device app so that anybody can access it. Sincethe UVI measurement could be misleading, e.g. if the user is in theshade, the app takes into account the forecasted UVI and/or thetemperature of the device to decide if the measurement is valid or not.If it is valid then it is pushed to the servers. Otherwise it isdiscarded.

The mobile device also provides a user interface that allows user tochange profile settings (e.g. age, gender, height, weight, etc.) and/orprogramming parameters. For example, user can select options and/orparameters that affect the calculation of UV dose and vitamin Dproduction, such as clothing coverage, sunscreen type, etc.

Method for Multi-user Management

Multiple users can take advantage of one single wearable unit. Themobile application can create several accounts protected by a password.Each account saves its own data associated with one individual user. Inaddition, each account can create several profiles for the associateduser. A profile is a configuration for a specific measurement of UV doseand/or vitamin D production. For example, it can include the clothestype worn and the sunscreen SPF used. Profiles are used to avoid settingthe same parameters multiple times.

The mobile application can also track several wearable units at the sametime. Each wearable unit can trigger a different alarm and an electronicleash allows the app to detect when a wearable unit is out of range bysetting a sound, vibration, or light alarm on the mobile device and/orthe wearable unit. Multi-user tracking is convenient, for example, for aparent or a caregiver to monitor UV exposure of several children. Inaddition, by utilizing the electronic leash function, the mobile devicecan operate in a search mode that allows the mobile app to find a lostwearable unit.

Optimal Balance of UV Exposure

This invention provides a novel solution for monitoring a person's UVexposure, and using such information to provide feedback and guidance tothe user to optimize his/her sun experience. Specifically, in thissection, we describe a method which can be implemented in the UVdosimetry system to quantitatively evaluate the optimal balance of UVexposure, by taking into account of both skin damage and vitamin Dproduction effects of UV light. The described new metrics can becalculated and displayed in real-time by a UV dosimetry system thatprovides a visual feedback and guidance to the user for achievingoptimal balance between the risk and benefits of UV exposure.

One new metric is termed UV exposure score (UVES), which is a normalizedindex ranging from 0 to 1 (or 0-100 by applying a scale factor 100).Higher UVES indicates improved UV balance whereas lower UVES indicatesless optimal UV balance (obviously, it is equivalent to define UVES inthe complementary form 1-UVES, so that lower value indicates improved UVbalance whereas higher value indicates less optimal UV balance). Forexample, the UVES can be defined as:UVES=pvd^(V)·(1−puv^(U))  (1)where pvd is the percentage of vitamin D production toward the dailygoal, puv is the percentage of accumulated UV dose against the limit ofsafe UV dose, and V and U are two positive parameters that can bepredefined or programmed by the user.

Clearly, UVES is the product of two terms, pvd^(V) and 1−puv^(U), whichrespectively quantify the benefit of UV (e.g. vitamin D production) andresidual risk of UV damage (e.g. sunburn). The weight of these two termscan be adjusted by parameters V and U, which can be predefined orprogrammed by the user of the UV dosimetry system.

The method of calculating vitamin D production is described below in thesection titled “Calculation of Cutaneous Vitamin D Synthesis.” The dailygoal of vitamin D production (in unit of IU) is defined by the user, andpvd^(V) is limited to the upper ceiling 1.0 (i.e. pvd^(V) is fixed to 1when the synthesis of vitamin D exceeds the daily goal). The method ofcalculating the accumulated UV dose is described below in the sectiontitled “Calculation of Safe Sun Exposure Time.” The limit of safe UVdose is defined by the user, typically being set to the user-specificminimal erythemal dose (MED). Similarly, puv^(U) is limited to the upperceiling 1.0 (i.e. 1−puv^(U) is fixed to 0 when the accumulated UV doseexceeds the limit of safe UV dose).

For the purpose of illustration, FIG. 3 shows the 3D view of UVES as afunction of pvd and puv. Alternatively, a 2D view of UVES can bedisplayed in grey scale or color-coded. As examples, two simulatedtrajectories of accumulated UV dose and amount of vitamin D productionover a period of time are also shown. As can be seen, while onetrajectory 810 progressively reaches the optimal state (highUVES)—evidenced by exceeding the goal of vitamin D production (pvd>100%)and not reaching limit of safe UV dose (puv<100%), the other trajectory820 fails to climb to the optimal state—the vitamin D production is lessthan 70% of the daily goal, and the accumulated UV dose is near thelimit of safe UV dose. Therefore, the composite metric UVES provides aquantitative means to characterize the degree of balance between thebenefit and risk of UV exposure. Users can use this metric as a guidanceto optimize their sun exposure. In addition, daily log of the UVES canprovide a historical view and trending analysis of the overall sunexposure experience.

An alternative metric is termed UV balance index (UVBI), which rangesfrom −1 to 1. More negative UVBI (approaching −1) indicates lowervitamin D production, whereas more positive UVBI (approaching 1)indicates higher risk of sunburn (obviously, it is equivalent to defineUVBI using the opposite form −UVBI, so that more positive valueindicates lower vitamin D production, whereas more negative valueindicates higher risk of sunburn). The optimal balance of UV exposure isreflected by UVBI value that is close to zero. Unlike UVES, UVBI candifferentiate two sub-optimal UV exposure conditions (low production ofvitamin D vs. high risk of sunburn). As an example, UVBI can be definedas:UVBI=(1−puv^(U))(pvd^(V)−1)+W·puv^(U)  (2)where W is a weighting function that as described below. Similarly, pvdis the percentage of vitamin D production toward the daily goal, puv isthe percentage of accumulated UV dose against the limit of safe UV dose,and V and U are two positive parameters that can be predefined orprogrammed by the user. Likewise, both pvd^(V) and puv^(U) have theupper ceiling of 1.0.

Note the first term (1−puv^(U))(pvd^(V)−1) approaches 0 when eitherpuv^(U) or pvd^(V) approaches 1, thus UVBI will be determined by thesecond term W·puv^(U) in these two conditions. The weighting function Wcan be user defined such that it satisfies the following criteria:

-   -   (a) If puv approaches or exceeds 1 (puv^(U)→1), then UVBI        approaches 1.    -   (b) If puv is sufficiently smaller than 1 (puv^(U)1−ε, where        0<ε<1) AND pvd approaches or exceeds 1 (pvd^(V)→1), then UVBI        approaches 0.    -   (c) Otherwise, UVBI approaches −1 as pvd approaches 0        (pvd^(V)→0).

One exemplary definition of the weighting function is to set W=0 ifcondition puv<0.85 OR (puv<1 AND pvd<0.75) is met, or set W=puvotherwise. Another exemplary definition of the weighting function is toset W=0 if condition puv<0.75 OR (puv²+pvd²<1.44 AND pvd<0.9) is met, orset W=puv otherwise. The purpose of conditional setting W to zero is toavoid or minimize the effect of numerical cancellation between the firstand second terms in equation (2). Clearly, the weighting function can bedefined by many other means as long as the three criteria describedabove are met.

Calculation of Safe Sun Exposure Time

For a wearable UV dosimetry system, an essential feature is to predicthow long the user can safely stay exposed to sun light before gettingsun burn. The time to skin burn is a dynamic parameter. It is affectedby the time varying intensity of UV irradiance. It also depends on theaccumulated UV dose already received by the user. Moreover, it can bemodified by the user's behaviors, such as seeking the shade, changingclothing coverage, applying the sunscreen, etc. This section describes amethod for dynamic estimation of the safe sun exposure time in a UVdosimetry system.

The UV sensor embedded in the UV sensing unit generates output of UVintensity (unit: J/m²) over time, which can be represented as a timeseries that has the sampling interval T_(s). The device then calculatesthe accumulated UV dose by integrating the UV intensity over time basedon the trapezoidal rule.

$\begin{matrix}{{D(t)} = {\left\lbrack {\sum\limits_{k = 0}^{t}\;{\left( {I_{k} + I_{k + 1}} \right) \cdot \left( {t_{k + 1} - t_{k}} \right)}} \right\rbrack/2}} & (3)\end{matrix}$where D(t) is the accumulated dose from predefined time 0 until time t,I_(k+1) and 1_(k) are respective UV intensity readings at twoconsecutive sample times t_(k+1) and t_(k). The accumulated UV dose andthe time are reset at predefined time of day. For example, the elapsedtime is typically reset to 0 at midnight, and the accumulated UV dose iscleared at the time reset.

Typically, t_(k+1)−t_(k)=T_(s) when all regularly sampled data areavailable, but the time gap may be greater than T_(s) in case of misseddata, e.g. due to data loss during wireless transmission or rejection ofinvalid data due to measurement noise. When the time gap is greater thana predefined threshold Δ, the UV intensity can be assumed to be zerowithin the time gap. Alternatively, the UV intensity can be assumed toremain the same as the last valid sample. Yet another alternative is toassume the UV intensity gradually decrease from the last valid sample tozero in a linear or nonlinear fashion.

Assuming there is no additional protection (e.g. sunscreen use orclothing coverage) for the skin area near the UV sensing unit, the safesun exposure time can be expressed in terms of time to skin burn (T2SB),which is defined as:T2SB=[p·MED−D(t)]/b  (4)where p is a positive coefficient that is user-programmable, MED is thepredefined minimal erythemal dose that is dependent on user's skin typeaccording to the Fitzpatrick scale, and b is the projected slope of UVdose accumulation.

Note the product p·MED defines the maximum UV dose that is consideredsafe for the user, where the parameter p allows user to specify thescale in terms of MED that the user can tolerate. For example, themaximum safe UV dose is 1 MED when p=1, or 1.5 MED when p=1.5, or 0.9MED when p=0.9. As illustrated in FIG. 4, the projected slope of UV doseaccumulation may be obtained based on the linear regression of the mostrecent UV dose accumulation curve. Denote T the time vector ranging fromtime t−δ to time t, and D the UV dose vector ranging from D(t−δ) at timet−δ, to D(t) at time t, where δ can be a predefined constant or auser-programmable parameter, then the projected slope b is calculatedas:b=[NΣ(D·T)−(ΣD)·(ΣT)]/[N(ΣT ²)−(ΣT)²]  (5)where N is the total number of samples between time t−δ and time t(note: each sample has a pair of sample time and UV dose value).

Two special cases need to be considered when t<δ (i.e. at the beginningof the day, shortly after the midnight) or when D(t)−D(t−δ)=0, whichmeans there is no UV exposure in the past δ duration, hence the slopeb=0 (i.e. the UV dose segment is flat). In such cases, the projectedT2SB is infinite.

If the UV dose increases after a flat segment, equation (5) can besimilarly used to calculate the new slope. However, the vector length ofT and D are reduced, and then they gradually increase until reaching thefull length N. This can be illustrated in FIG. 5, which shows a portionof UV dose curve with a flat segment followed by an upward increase. Inthis example, each sample of UV dose curve (solid line) is marked by acircle. The UV dose curve remains flat until its last sample 840. Thenthe UV dose curve starts to increase from sample 841. For illustrationpurpose, the vector length N is set to 8 in this example. The slope bremains zero throughout the flat segment, including its last 8 samples850. Upon detecting an increase of the UV dose at sample 841, the vectorlength is reduced to 4 in this example. Thus the slope is evaluatedusing only a group of 4 samples 851. As new UV dose values are availableafter sample 841, the vector length starts to increase toward itsoriginal length (N=8). As illustrated in the figure, for the sample 842,the slope is evaluated using a group of 5 samples 842; for the sample843, the slope is evaluated using a group of 6 samples 843; for thesample 844, the slope is evaluated using a group of 7 samples 854; andfor the sample 845, the slope is evaluated using a group of 8 samples855. This adaptive adjustment of vector length for calculating slope isimportant because while the linear regression allows smooth projectionof T2SB (i.e. noise tolerant), shortening vector length after a flatsegment allows quick adaptation of rising UV dose curve after suddenexposure to sunlight.

Yet in another embodiment, the projected slope of UV dose accumulationis based on the current or recent UV intensity, instead of based onlinear regression analysis of the UV dose accumulation curve.Specifically, the projected slope of UV dose accumulation is the productof UV intensity (I) and the sampling interval (T_(s)), i.e. b=I×T_(s).This assumes that the UV intensity remains the same for the foreseeablenear future. In one example, I is the device measured current UVintensity. In another example, I is the extrapolated UV intensity valuebased on the most recent measurement of UV intensity in a short timewindow. The method of calculating the extrapolated UV intensity value ina short time window is described below in the section titled“Extrapolation of UV Sensor Output”. Yet in another example, I is theforecasted or crowd sourced UV intensity value that is time and locationspecific to the user. This is useful when the user is staying indoor orin the shade but wants to have a rough estimate of T2SB should he or shegoes outside to get exposed to sunlight, without actually measuring theUV intensity using the device.

When the UV sensor is not aligned with the direct solar irradiance,measurements may understate the UV intensity at surface that is normalto the sun light. Therefore, the measured UV intensity, as well as thecalculated accumulated UV dose D(t) will vary depending on where the UVsensing unit is worn (or maybe the sensor is just located nearby but notworn on the body). There are many variables affecting the UV irradianceangle, such as the solar zenith angle, the orientation and body movementof the subject, and the environment surrounding the subject (e.g. shade,light reflections, etc.). While it may be impractical to account for allthese factors in real time, scaling the sensor measured UV intensity ordose by a sensor location factor (SLF) may provide a reasonable estimateof the UV intensity or D(t) at different body surface areas. Forexample, if the UV sensor is worn on user's wrist facing outward, thesensor measured UV dose at the wrist will need to be multiplied by asite-specific SLF to derive the UV dose at an uncovered body area, suchas the face, shoulder, chest, legs, etc. The determination of SLF atdifferent body parts is ideally through a calibration phrase, when aplurality of UV sensors are worn by the user at different locations fora certain period of time, and the ratios between UV dose readings fromthese sensors are used to determine site-specific SLF. Typically,default values of SLF for different sensor locations can be predefinedbased on an experimental study involving a group of subjects. Similarapproach is described in the paper authored by Thieden et al. “The wristis a reliable body site for personal dosimetry of ultravioletradiation.” Photodermatol Photoimmunol Photomed 2000; 16: 57-61. Asexpected, a user may experience skin burn earlier on the face orshoulder than on the arms or legs because the actual UV dose variesamong different body parts. Therefore, the safe sun exposure time T2SBwill depend on which part of the body surface is uncovered, and iscalculated based on the highest accumulated UV dose D(t) that isadjusted for SLF.

Another factor that affects the safe sun exposure time is the topicalapplication of sunscreen. Usage of sunscreen with high skin SPF cansignificantly block or absorb UV radiation, thus prolong the time toskin burn (T2SB). In general, for an uncovered skin area with adequatesunscreen application, the safe sun exposure time calculated above isadjusted by multiplying the SPF of the sunscreen. Thus, if thecalculated T2SB without sunscreen is 10 minutes, then the safe sunexposure time will be 150 minutes after proper application of sunscreenwith the SPF 15. Note, however, the effective SPF is not only dependenton the product labeling, but also the frequency and amount of sunscreenapplied, the binding property of the sunscreen, the elapsed time sincelast sunscreen application, and so on. The method for calculatingeffective SPF is described below in the section titled “Calculation ofEffective SPF”.

Calculation of Cutaneous Vitamin D Synthesis

A UV dosimetry system that can calculate the daily vitamin D productionresulting from solar UV exposure will provide useful information for theuser to determine if vitamin D supplement is necessary, and if so, howmuch is necessary. Such quantitative information will offer userobjective guidance to achieve optimal balance of sun exposure, andprescribe behavioral changes in maintaining personal health. Thissection describes a method for calculating daily vitamin D productionfrom UV light in a UV dosimetry system.

The formula for calculating the amount of vitamin D produced by peoplefrom an average daily UV exposure was originally described by Godar etal. (Environ Health Perspect 2012; 120:139-143). Instead of relying ondaily average UV dose derived from a sample population, more accurateestimation of individual's daily cutaneous vitamin D synthesis (DVD) dueto UV exposure can be obtained by integrating the subject'sinstantaneous vitamin D production rate over a day using the trapezoidalrule:

$\begin{matrix}{{DVD} = {\sum\limits_{k}\;{\left\lbrack {\left( {V_{k} + V_{k + 1}} \right) \cdot \left( {t_{k + 1} - t_{k}} \right)} \right\rbrack/2}}} & (6)\end{matrix}$where V_(k+1) and V_(k) are respective vitamin D production rate at twoconsecutive sample times t_(k+1) and t_(k). The DVD and time are resetat predefined time of day. For example, the elapsed time is typicallyreset to 0 at midnight, and DVD is cleared at the time reset andaccumulates over 24 hours. Obviously, vitamin D production overdifferent time intervals (e.g. hourly, etc.) can be similarlycalculated. Typically, t_(k+1)−t_(k) equals the device sampling intervalwhen all regularly sampled data are available, but the time gap may begreater than the sampling interval in case of missed data, e.g. due todata loss during wireless transmission or rejection of invalid data dueto measurement noise. When the time gap is greater than a predefinedthreshold A, the vitamin D production rate can be assumed to be zerowithin the time gap. Alternatively, the vitamin D production rate can beassumed to remain the same as the last valid sample. Yet anotheralternative is to assume the vitamin D production rate graduallydecrease from the last valid sample to zero in a linear or nonlinearfashion.

At any given time instant, the vitamin D production rate (V) iscalculated as:

$\begin{matrix}{V = {C \cdot {cf} \cdot {sf} \cdot {af} \cdot {bf} \cdot {\sum\limits_{s}\;\left( {I_{s} \cdot {{pBSA}_{s}/{eSPF}_{s}}} \right)}}} & (7)\end{matrix}$where C=49 is a constant which is derived from a reference valuecorresponding to the approximate amount of vitamin D generated (measuredin ×100 IU) by a typical young female subject having Fitzpatrick skintype II with a whole body exposure to nearly uniform UV irradiance atthe intensity of 320 J/m². As described in details below, cf is theaction spectrum conversion factor, sf is the skin type factor, of is theage factor, and bf is the body surface area factor. The summation termrepresents the total contribution of vitamin D production from allexposed body surface area, which is divided into multiple parts (e.g.face, chest, arms, legs, etc.). For each part of exposed body surfacearea (s), the corresponding UV intensity (I_(s)), its percentage oftotal body surface area (pBSA_(s)), and the effective sun protectionfactor (eSPF_(s)) of the applied sunscreen are considered for thecalculation.

The action spectrum conversion factor cf changes erythemally weighted toprevitamin D3 weighted UV doses. For general calculation, cf can besimplified to be a constant 1.0, that is, the differences betweenwavelength contributions estimated by the erythmal action spectrum andthe previtamin D action spectrum are ignored. Alternatively, cf can bedetermined by linear interpolation of values reported by Pope et al(Photochem Photobiol 2008; 84:1277-1283), by taking into account ofseason and the user's latitude.

Increased skin pigment can greatly reduce the capacity of skin tosynthesize vitamin D. Thus the UV dose required to make the same amountof vitamin D varies by skin type. Using Fitzpatrick skin type II as thereference, the skin type factor sf can be determined, as described byGodar et al. (Environ Health Perspect 2012; 120:139-143), based on therespective minimum erythmal dose (MED) values.

It has also been known that aging significantly affects the capabilityof human skin to produce vitamin D. Compared to a 20 years old adult, a70 years old person has about 75% reduced capacity to make vitamin D inthe skin. The age-dependent capability of vitamin D production can becharacterized by age factor af which can be fixed to 1.0 for age <20years and 0.10 for age >80 years, and decreases following a linear modelfor age between 20-80 years:af=1−0.015·(age−20)  (8)

The amount of vitamin D production in a person depends on how much skinis exposed to UV light. Given the same percentage of skin area exposure,a person with a larger body surface area (BSA) will produce more vitaminD than a person with a smaller BSA. The body surface area factor bf isthe ratio of a person's BSA to 1.7 m², which is approximately the BSA ofa typical female subject. A person's BSA can be calculated based on theformula of DuBois and DuBois. Hence the body surface area factor bf iscalculated as:bf=0.20247×H ^(0.725) ×W ^(0.425)/1.7  (9)where the person's height (H) is in meters and weight (W) is inkilograms.

Only skin surface exposed to UV light can synthesize vitamin D. Thetotal BSA can be divided into different parts, for example, based on theLund and Browder chart, which specifies the approximate percentage ofeach part's surface area relative to the total BSA. Different clothingchoices can affect the percentage of body surface area that is exposed.Specification of various body parts and their corresponding percentagesof body surface area corresponding to common clothing choices aredescribed in the following section titled “Calculation of Exposed BodySurface Area”.

In a simplified scenario, the UV intensity measured by the wearable UVsensor is assumed to be uniform over the exposed body surface, and thesunscreen application is also assumed to be uniform. Then equation tocalculate the vitamin D production rate can be simplified to:V=C·cf·sf·af·bf·I·pBSA/eSPF  (10)where I is the UV intensity, pBSA is the percentage of total BSA beingexposed to UV light, and eSPF is the effective SPF of the appliedsunscreen. Note the effective SPF is not only dependent on the productlabeling, but also the frequency and amount of sunscreen applied, thebinding property of the sunscreen, the elapsed time since last sunscreenapplication, etc. The method for calculating eSPF is described below inthe section titled “Calculation of Effective SPF”.

Also note that the measured UV intensity at the sensor wearing locationmay be different from the actual UV intensity at a different body areadue to varying solar irradiance angle. As described above in the sectiontitled “Calculating of Safe Sun Exposure Time”, scaling the sensormeasured UV intensity by a sensor location factor (SLF) may provide areasonable estimate of the UV intensity at different body surface areas,and the SLF at different body parts can be determined through acalibration phrase. As another example, a user may apply sunscreen oversome, but not all exposed body surface areas. Thus the eSPF at differentparts of body surface may vary significantly.

Knowing the vitamin D production rate (V) and the daily goal of vitaminD amount (DGVD) which can be programmed by the user, the device can alsoestimate the time remaining to reach the DGVD (T2VD). Specifically, atany time of the day, T2VD can be calculated by dividing the differencebetween DGVD and the accumulated vitamin D amount at that time by theproduct of sampling rate and the projected vitamin D production rate(V). This calculation can be performed in a way that is similar to themethod of calculating the time to skin burn (T2SB) as described above inthe section titled “Calculation of Safe Sun Exposure Time”. For example,at any time of the day, the device can calculate the projected vitamin Dproduction rate (V) based on linear regression analysis of the vitamin Daccumulation curve in the preceding time interval. Alternatively, thedevice can calculate the projected vitamin D production rate (V) basedon the current UV intensity measured by the device, or the extrapolatedUV intensity value based on the most recent measurement of UV intensityin a short time window. The method of calculating the extrapolated UVintensity value in a short time window is described below in the sectiontitled “Extrapolation of UV Sensor Output”. Yet in an alternativeembodiment, the projected vitamin D production rate (V) is calculatedbased on the forecasted or crowd-sourced UV intensity value that is timeand location specific to the user. This could be useful when the user isstaying indoor or in the shade but wants to have a rough estimate ofT2VD should he or she goes outside to get exposed to sunlight, withoutactual measuring the UV intensity using the device.

Calculation of Effective SPF

As noted above, the effective SPF of a sunscreen product often actuallydeviates from the labeled SPF, due to some common factors including theamount of sunscreen initially applied, binding property of thesunscreen, reapplication of the sunscreen, and time elapsed sinceprevious sunscreen application, etc. This section describes a method forcalculating the effective SPF in a UV dosimetry system.

After initial application of sunscreen, it is removed from the skin byclothing or sweating such that the thickness remaining on the skindecreases with time in an exponential manner. This can be mathematicallymodeled by:x(t)=x(0)·exp(−λt)  (11)where x(t) is the sunscreen thickness at time t, x(0) is the initiallyapplied sunscreen thickness, λ is rate constant for sunscreen removalthat is related to the substantivity half-life of the sunscreen, whichcharacterizes the binding capability of the sunscreen, and can beapproximately determined based on such factors as the type of sunscreen,and the activity of the subject (toweling, sweating, swimming, etc.).When sunscreen is reapplied at time T, the newly applied sunscreenamount α is added to the remaining sunscreen x(T), and the time courseof sunscreen thickness follows the same exponential curve for t>T.

The relationship between sunscreen application thickness and theresulting SPF has been investigated by a number of studies. The resultshave been variable ranging from an exponential relationship between SPFand applied thickness, to a logarithmic relationship, and through to anapproximately linear relationship. Depending on the spectral absorptionprofile of the product, the relationship could vary. For example, alinear relationship of the SPF and the sunscreen application thicknesscan be modeled by:SPF_(E)=1+(SPF_(L)−1)·x/2  (12)where SPF_(E) denotes the effective SPF, SPF_(L) is the labeled SPF ofthe sunscreen product, and x is the sunscreen thickness which can becalculated from equations described above. Based on this model, thesunscreen achieves its labeled SPF value when the application amount is2 mg/cm² as specified in the international protocol. The unit intercepton SPF axis implies no additional sun screen protection when nosunscreen is applied (i.e., x=0).Calculation of Exposed Body Surface Area

To estimate the amount of cutaneous vitamin D synthesis, it is importantto calculate the percentage of exposed body surface area, taking intoaccount of various options of clothing coverage. This section describesthe method to calculate the exposed body surface area in a UV dosimetrysystem.

A person's total body surface area (BSA) can be calculated using theDuBois and DuBois formula.BSA=0.20247×H ^(0.725) ×W ^(0.425)  (13)where the person's height (H) is in meters and weight (W) is inkilograms. The total BSA can be divided into different body parts basedon the Lund and Browder chart, which specifies the approximatepercentage of each part's surface area relative to the total BSA and isknown to be age dependent.

Age is a main factor affecting the percentage values in the Lund andBrowder chart because the ratio of the surface area of the head to thesurface area of the limbs is typically larger in children than that ofan adult. To estimate the surface area of various body parts differentages, linearly interpolated values can be found between ages on theLund-Browder charts.

Different clothing choices can affect the percentage of total BSA thatis exposed. The UV dosimetry system may allow user to select differentclothing combinations for different parts of the body by browsingthrough or toggling between various clothing options. For example, theuser can toggle between “with hat” and “no hat” to indicate coverage orexposure of head. Likewise, the user can select “bare chest”, “shortsleeve”, or “long sleeve” to represent different degrees of upper bodycoverage. Clothing coverage for other parts of the body surface can besimilarly defined. The selection of clothing options can vary in numberand styles. Each selected clothing option can have a predefined surfacearea coverage percentage for the selected body part.

A lookup table can be predefined that lists the approximate percentagevalues of skin exposure of various body parts as a result of commonclothing choices. An empty cell in the table indicates the skin surfaceof the body part (e.g. in the row) is not affected by the correspondingclothing option (e.g. in the column) In contrast, a value of zero in thecell means no skin exposure for the corresponding body part, i.e. it iscompletely covered by the clothing. To calculate the percentage of aperson's exposed BSA relative to the total BSA, each body part's BSApercentage (obtained from the Lund and Browder chart) is multiplied bythe body part's skin exposure percentage value based on thecorresponding clothing choice, and then these weighted terms are summed.

Extrapolation of UV Sensor Output

While it is common sense that the UV sensor should not be obstructedfrom the sun for accurate measurement, in practical use the device mayexperience changing angle of the irradiating sunlight and/or temporaryshading of the device. The design of a UV dosimetry system shall takethis variability into account, to at least partially compensate for thebody movement and temporary obstruction of the UV sensor. This sectiondescribes a method to filter out the unreliable UV measurement in a UVdosimetry system based on signal processing of the sensor generated UVintensity data.

The UV sensor embedded in the UV dosimetry system generates real timeoutput of UV intensity. To estimate the actual UV intensity irradiatedon the subject, the UV intensity measured by the UV sensor can beextrapolated by means of statistical analysis of the data, based on twobasic assumptions:

(1) The actual UV intensity received by the subject is quasi-static,that is, the rate of change of UV intensity irradiated on the subject isslow over a short duration.

(2) When the subject is exposed to the sunlight, the wearable UV sensorwill have proper alignment with the solar irradiance at least for a verybrief period of time during the short duration when actual UV intensityis stable.

Therefore, although the wearable UV sensor may often measure lower thanactual UV intensity due to the changing body position and/or shadecoverage, as long as the user is non-stationary so that the sensor canhave good alignment with the sunlight from time to time, even ifbriefly, the device may have correct or nearly correct measurement ofthe actual UV intensity. On the other hand, it is important to note thateven though the sensor measured UV intensity is below the actual value,the underestimation may be limited since it is likely that not allexposed body surface is normal to the sunlight. In other words, part ofthe exposed body surface may indeed receive less UV exposure due to itsnon-normal orientation and/or shade coverage.

Based on the above assumptions, the actual UV intensity irradiated onthe subject over a short period of time can be extrapolated based on thelimited numbers of correct measurement of UV intensity obtained duringthat short time window. The extrapolation method is described below withreference to FIG. 6.

Assume the device measures M samples of UV intensity values over a shorttime window T (e.g. T=60 s, corresponding to M=60 for a samplingfrequency of 1 Hz). Data extrapolation performed in this time window (asdescribed below) will be repeated for the next time window, which can beoverlapping or non-overlapping with the current time window. Aprogrammable UV intensity threshold TH (e.g. 0.5 UVI, or 12.5 mW/m²)defines the boundary above which the measured UV intensity is likelymore representative of the actual value whereas below which the sensoroutput is likely an underestimate of the true UV intensity. Furtherdenote the maximal UV intensity value among the M samples is UVMAX,which is assumed to be obtained when the sensor is best aligned with thesolar irradiance during the short time window T. Also, when the devicecomprises multiple UV sensors that measure the UV intensity at differentangles, the UVMAX can be taken as the maximum UV intensity valuemeasured by all UV sensors during the short time window.

The device counts the number of samples (G) in the short window T thathave measured UV intensity greater or equal to the threshold TH. Highercount of G indicates higher probability and confidence that the measured≧TH values reflect the true UV intensity whereas smaller G implies viceversa. The count G is compared to another predefined thresholds C (e.g.C=8 for M=60). If the count G is greater than 0 (i.e. at least onesample of measured UV intensity is above TH), then the extrapolated UVintensity of all M samples in the short time window T is set toMIN(G,C)/C×UVMAXwhere MIN( ) is the function that returns the minimum value. In otherwords, the extrapolated UV intensity of all M samples is set to UVMAX ifG≧C. If 1<G<C, then the extrapolated UV intensity of all M samples isset to a fraction of the UVMAX, in proportion to the count G. On theother hand, if G=0 (i.e. no sample has measured UV intensity above TH),then the extrapolated UV intensity values of the M samples remainunchanged from the measured UV intensity values.Calculation of Sun Exposure Time

This section describes a method for calculating the daily sun exposuretime (SET) in a UV dosimetry system. This feature may not only revealthe likely correlation between a user's mood and SET, but also providesthe user guidance to enhance mood by consciously adjusting SET.

The UV sensor embedded in the wearable device generates output of UVintensity over time which can be converted to the time series of UVindex (UVI). The daily SET is defined as the total duration of the daywhen the sensor measured UVI is greater than a predefined lowerthreshold (UVI_(LT)).

Denote UVI1 and UVI2 the respective UVI readings of the device at twoconsecutive time points t1 and t2 (t2>t1). Further denote dt theduration between t1 and t2 that contributes to SET, which is the timeperiod between t1 and t2 when UVI is greater than or equal to UVI_(LT).Based on the values of UVI1 and UVI2, dt can be calculated according todifferent conditions: (1) dt=0 if both UVI1 and UVI2 are less thanUVI_(LT) (i.e. no sun exposure between t1 and t2); (2) dt=t2−t1 if bothUVI1 and UVI2 are greater than or equal to UVI_(LT) (i.e. continuous sunexposure between t1 and t2); (3) dt is calculated as a fraction of theduration between t1 and t2 based on linear interpolation if UVI1 isbelow while UVI2 is above UVI_(LT), or if UVI2 is below while UVI1 isabove UVI_(LT). Yet in a simpler version, the calculation of dt incondition (3) above can be approximated as (t2−t1)/2. This isappropriate when sampling interval is sufficiently small.

During normal operation, the device resets SET to zero at midnight, andaccumulates dt into SET anytime when device measured UVI is greater thanor equal to UVI_(LT) until the end of the day. Typically, the devicemeasures the UV intensity and converts it to UVI at the samplinginterval. However, the time gap between two consecutive measurements maybe greater than the sampling interval in case of missed data, e.g. dueto data loss during wireless transmission or rejection of invalid datadue to measurement noise. When the time gap is greater than a predefinedthreshold A, the UVI may be assumed to be zero within the time gap.Alternatively, the UVI can be assumed to remain the same as the lastvalid sample. Yet another alternative is to assume the UVI graduallydecrease from the last valid sample to zero in a linear or nonlinearfashion.

Method to Predict Optimal Sun Exposure Time

While it is helpful for a person to obtain instant feedback on receivedUV dose and cutaneous vitamin D production, and use such information asa guidance to optimize sun exposure, more benefits can be gained if theperson can plan ahead for optimal sun experience. This section describesa method for predicting the effects of sun exposure to the user in thenext few days in a UV dosimetry system.

According to a preferred embodiment of this invention, the UV dosimetrysystem retrieves forecasted UVI data for the next few days correspondingto the user's location from trusted sources or official websites, e.g.,www.epa.gov, www.weather.gov, etc. The user's location can beautomatically determined via GPS integrated in the mobile computingdevice of the UV dosimetry system. In addition, the user's location canbe specified by the user through the input interface of the UV dosimetrysystem. This is helpful if the user plans to travel to a differentlocation and wants to predict the effects of sun exposure at the newlocation. The forecasted UVI data often has limited temporal and spatialresolution. For example, the forecasted UVI data is often presentedhourly, and it is only presented for selected cities and/or areasassociated with certain zip codes. To overcome this challenge, linear ornonlinear (e.g. spline) interpolation can be used to obtain forecastedUVI data at specific time and location with higher resolution. Forexample, FIG. 7 illustrates the concept of temporal interpolation of UVIdata. In this example, the forecast 24-hour UVI data is available for aspecific location. While the forecast data is only available hourly(labelled in circles), the UVI or UV intensity at any given time of day915 (labelled in a triangle) can be interpolated between UVI values ofadjacent time points 910 and 920. Since 1 UVI corresponds to UVintensity of 25 mW/m², the interpolated UV intensity (which can beexpressed in continuous values) is preferred than interpolated UVI(which can only be expressed in discrete values). For spatialinterpolation of UVI, a geographic map can be generated with a specifiedlatitude-longitude grid which has a high spatial resolution. The UVI ata specific target location without forecast data can be interpolatedfrom neighboring grid points that have forecast UVI data available.Therefore, the UV dosimetry system can predict UV intensity at specificlocation and given time of day by means of interpolation of the forecastUVI data.

According to one embodiment of this invention, the UV dosimetry systemestimates the user received UV dose and generated vitamin D in the nearfuture (e.g. next day) based on the predicted UV intensity data for aplural of hypothetical UV exposure scenarios. Each hypothetical UVexposure scenario corresponds to a combination of start time andduration of UV exposure, and the corresponding user characteristics suchas the clothing coverage, sunscreen usage, and wearing location of theUV sensing unit. These hypothetical scenarios can be predefined by theuser, or can be edited by the user through the user interface of theportable computing device. For example, the user can predefine thescenarios based on his/her past habit or routine of UV exposure, oradd/delete/modify the scenarios arbitrarily, e.g. 20 minutes sunexposure starting at 9 am, 10 minutes sun exposure starting at 10 amplus 5 minutes exposure starting at 12 noon, etc. The usercharacteristics (e.g. clothing coverage, sunscreen usage, and wearinglocation) associated with each episode of UV exposure can be predefinedor edited by the user. Therefore, for each of the hypothetical UVexposure scenarios, the UV dosimetry system can estimate thecorresponding UV dose received by the user and vitamin D production. Themethod of calculating the accumulated UV dose is described above in thesection titled “Calculation of Safe Sun Exposure Time”, and the methodof calculating vitamin D production is described above in the sectiontitled “Calculation of Cutaneous Vitamin D Synthesis.” Based on theestimated UV dose and vitamin D production, the UV dosimetry system canevaluate the composite metric UVES and/or UVBI for each of thehypothetical UV exposure scenarios as described above in the sectiontitled “Optimal Balance of UV Exposure”. Consequently, the UV dosimetrysystem can provide the user a ranked list of hypothetical UV exposurescenarios based on the calculated composite metric UVES and/or UVBI,such that the scenario ranked high on the list is more preferred (i.e.more balanced with respect to the risk of sun burn and benefit ofvitamin D production) than the scenario ranked low on the list, and theuser can use such a ranked list as a guide to plan ahead for future sunexposure.

According to another embodiment of this invention, the UV dosimetrysystem can estimate the safe sun exposure time, the amount of vitamin Dgenerated during the safe sun exposure time, and the duration of sunexposure needed to reach user-defined daily goal of vitamin Dproduction, for a plural of hypothetical UV exposure scenarios.Different from the embodiment described above, each hypothetical UVexposure scenario corresponds to a start time of UV exposure, and thecorresponding user characteristics (e.g. clothing coverage, sunscreenusage, and wearing location of the UV sensing unit), but the duration ofUV exposure is not defined. Similarly, these hypothetical scenarios canbe predefined by the user, or can be edited by the user through the userinterface of the portable computing device. For each of the hypotheticalUV exposure scenarios, the UV dosimetry system estimates the safe sunexposure time (e.g. time to sun burn), the amount of vitamin D generatedduring the safe sun exposure time, and the duration of sun exposureneeded to reach user-defined daily goal of vitamin D production. Forexample, the UV dosimetry system may estimate for one scenario, the userwill have 20 minutes safe sun exposure time, generate 800 IU of vitaminD during that period, and need only 10 minutes of sun exposure to reachthe user-defined daily goal of 400 IU vitamin D. Thus the user can planto enjoy the sun exposure, knowing he/she will get enough vitamin Dbefore having the risk of sunburn in this scenario. In another scenario,the UV dosimetry system may estimate that the user will have only 10minutes of safe sun exposure time, generate 500 IU of vitamin D duringthat period, and will need 25 minutes of sun exposure to reach theuser-defined daily goal of 1000 IU vitamin D. Thus the user can plan tolimit the sun exposure time and take vitamin D supplement for thisscenario. Similarly, the method of calculating the safe sun exposuretime is described above in the section titled “Calculation of Safe SunExposure Time”, and the method of calculating vitamin D production isdescribed above in the section titled “Calculation of Cutaneous VitaminD Synthesis.”

Method for Crowd Sourcing of UVI Data

According to this invention, UVI at various geographical locations ismeasured and updated in real-time by a large number of UV measurementdevices, or UV meters, by means of crowd sourcing method. People who areusing UV meters such as the disclosed UV dosimetry system, areencouraged to provide the real-time UVI data together with the time(e.g. using the build-in clock of the mobile device) and locationinformation (e.g. via GPS embedded in the mobile computing device)through the wireless network to a remote server, for example, inexchange for a credit or discount coupon to be used toward certainproducts or services. Users who opt to provide data can be anonymous toprotect their identities.

The remote server maintains a database containing the data transmittedfrom the UV meters. A user “pushes” the measured data (UVI, time,location) to the remote server in triggered mode, automatic mode, ormixed mode. In triggered mode, the user initiates the data transmissionby sending a command through the user input interface of the mobilecomputing unit to instruct the mobile application start thecommunication session. In automatic mode, the mobile application isconfigured to exchange data with the remote server on periodic basis,e.g. at programmed time of the day or at programmed time intervals. Inmixed mode, both manual triggered and automatic data transmission aresupported. Frequency of the data transmission can be limited (e.g. notmore than 1 time per X minutes, where X is a programmable parameter thatdefines the shortest duration between two consecutive datatransmissions) to prevent repeated transmission of the same UVI data.

In one embodiment, the UV dosimetry system also has a build-intemperature sensing unit such as a temperature sensor, and the measuredtemperature data is also sent to the remote server, together with themeasured UVI data and the time and location information of themeasurement.

The remote server runs a database management software operative toanalyze the data transmitted from the UV meters including UVI, time,location, and/or temperature measurement, and generates UVI maps forother users to use. The UVI maps are preferably updated periodicallyaccording to a predefined time interval.

According to this disclosure, the remote server maintains a databasethat aggregates the data transmitted from the UV measurement devices.The database management software controls the quality of the datacollection through a plurality of filters with programmable parametersettings. Quality control of crowd sourced data is important becauseinvalid UVI measurements may be inadvertently sent by the users. Forexample, a person staying indoors or under the shade may not measure theactual UVI in the local area accurately. Such misleading UVI data shallbe excluded from the database.

In one exemplary embodiment, the database management software in theremote server retrieves forecasted UVI data and/or temperature data atdifferent time and geographical locations from trusted sources orofficial websites, e.g., www.epa.gov, www.weather.gov, etc. Usertransmitted UVI measurement and/or temperature measurement data arecompared with the forecast data at the corresponding time and location.Here, the corresponding time means that the difference between the timeof the forecast data and the time of the UVI and/or temperaturemeasurement is below a predefined time limit, e.g. 10 minutes, and thecorresponding location means the distance between the location of theforecast data and the location of the UVI and/or temperature measurementis shorter than a predefined distance limit, e.g. 1 mile Usertransmitted measurement data is deemed invalid if there is substantialdeviation between measured data and the corresponding forecast data,e.g. if they differ by more than a predefined tolerance value.

In another exemplary embodiment, the official forecast data is only usedas a reference to reject invalid measurement data when the sample sizeof the measurement data at specific time period and location is limited.As more user transmitted measurement data at specific time period andlocation is available and included in the database, the weight of usingofficial forecast data as a reference is reduced. Meanwhile, more weightis given to the user transmitted data, which can self-serve as thereference to reject invalid user input.

For example, when the sample size of UVI and/or temperature datameasured during a specific time period and from a specific location issufficiently large (e.g. greater than a predefined threshold), theexpected normal range of UVI and/or temperature at said specific timeperiod and location can be calculated based on the statisticaldistribution of the measured UVI and/or temperature data (e.g. the meanand standard deviation, or the median and inter-quartile range). Outliermeasurement data falling outside the expected normal range will then bedeemed invalid.

For the UVI (or temperature) data at specific time and specificlocation, the sample size is defined as the total number of UVI (ortemperature) data measured during a time period that is close to saidspecific time (e.g. within a predefined time interval), and from aregion that is centered around said location (e.g. within a predefineddistance).

Through crowd sourcing, the UVI data is updated in the remote serverdatabase in real-time, and the UVI map can be updated periodicallyaccording to a predefined frequency. As more users contribute to thedatabase, the reliability and accuracy of the data increases. Thecoverage of the UVI data on the map can be as broad as the users can goand the spatial resolution can be as fine as the GPS allows. It alsoallows people who do not have a UV measurement device to obtainreal-time UVI information by accessing the crowd sourced UVI databasethrough a communication network, such as the internet.

While the invention has been described with reference to exemplaryembodiments, it shall be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted withoutdeparting from the scope of the invention. In addition, manymodifications may be made to adapt a particular situation or componentto the teachings of the invention without departing from its scope.Therefore, it is intended that the invention not be limited to theparticular embodiment disclosed, but that the invention will include allembodiments falling within the scope of the appended claims.

We claim:
 1. A device comprising: a UV sensing unit comprising acircuitry for measuring irradiating UV intensity with a predefinedsampling interval; and a mobile computing unit calculating UV dose basedon the measured irradiating UV intensity, and predicting safe UVexposure time for a user of the device, wherein the UV dose is adjustedby a predetermined scaling factor according to a relative location ofthe UV sensing unit with respect to an exposed body surface area of theuser.
 2. The device of claim 1, wherein the safe UV exposure time iscalculated by dividing the difference between a user-programmablemaximum UV dose and the device measured UV dose by a projected slope ofUV dose accumulation.
 3. The device of claim 2, wherein the projectedslope of UV dose accumulation is obtained by means of linear regressionanalysis of a vector of most recent UV dose samples calculated by thedevice.
 4. The device of claim 3, wherein the vector of most recent UVdose samples has a predefined vector length, and the vector length istemporarily reduced after UV dose starts to increase after beingconstant for a user-programmable time window.
 5. The device of claim 2,wherein the projected slope of UV dose accumulation is set to theproduct of UV intensity value and the sampling interval.
 6. The deviceof claim 1, wherein the mobile computing unit is adapted to predict safeUV exposure time for multiple users.
 7. The device of claim 2, whereinthe safe UV exposure time is adjusted by multiplying an effective sunprotection factor corresponding to the user applied sunscreen.
 8. Amethod comprising: measuring UV intensity irradiating on a device with apredefined sampling interval; calculating UV dose based on the measuredirradiating UV intensity; and predicting safe UV exposure time for auser of the device, wherein the UV dose is adjusted by a predeterminedscaling factor according to a relative location of the device withrespect to an exposed body surface area of the user.
 9. The method ofclaim 8, wherein the safe UV exposure time is calculated by dividing thedifference between a user-programmable maximum UV dose and the devicemeasured UV dose by a projected slope of UV dose accumulation.
 10. Themethod of claim 9, wherein the projected slope of UV dose accumulationis obtained by means of linear regression analysis of a vector of mostrecent UV dose samples calculated by the device.
 11. The method of claim10, wherein the vector of most recent UV dose samples has a predefinedvector length, and the vector length is temporarily reduced after UVdose starts to increase after being constant for a user-programmabletime window.
 12. The method of claim 9, wherein the projected slope ofUV dose accumulation is set to the product of UV intensity value and thesampling interval.
 13. The method of claim 8, wherein the device isadapted to predict safe UV exposure time for multiple users.
 14. Themethod of claim 9, wherein the safe UV exposure time is adjusted bymultiplying an effective sun protection factor corresponding to the userapplied sunscreen.
 15. A system comprising: a wearable unit comprising acircuitry for measuring irradiating UV intensity with a predefinedsampling interval; and a mobile computing device communicating with thewearable unit, calculating UV dose based on the measured irradiating UVintensity, and predicting safe UV exposure time for a user of thesystem, wherein the UV dose is adjusted by a predetermined scalingfactor according to a relative location of the wearable unit withrespect to an exposed body surface area of the user.
 16. The system ofclaim 15, wherein the safe UV exposure time is calculated by dividingthe difference between a user-programmable maximum UV dose and thesystem measured UV dose by a projected slope of UV dose accumulation.17. The system of claim 16, wherein the projected slope of UV doseaccumulation is obtained by means of linear regression analysis of avector of most recent UV dose samples calculated by the device.
 18. Thesystem of claim 16, wherein the projected slope of UV dose accumulationis set to the product of UV intensity value and the sampling interval.19. The system of claim 15, wherein the mobile computing device isadapted to predict safe UV exposure time for multiple users.
 20. Thesystem of claim 16, wherein the safe UV exposure time is adjusted bymultiplying an effective sun protection factor corresponding to the userapplied sunscreen.